Découvrez la première version de notre publication scientifique "Évaluation comparative des modèles de langage open-source pour une réponse efficace aux questions dans les applications industrielles" publiée dans arxiv et soumise à la revue Engineering Applications of Artificial Intelligence. Cet article, rédigé en anglais, est déjà disponible au public.
Merci à l'équipe de recherche de Novelis pour leur savoir-faire et leur expertise.
A propos
In the rapidly evolving landscape of Natural Language Processing (NLP),Large Language Models (LLMs) have demonstrated remarkable capabilitiesin tasks such as question answering (QA). However, the accessibility andpracticality of utilizing these models for industrial applications pose signif-icant challenges, particularly concerning cost-effectiveness, inference speed,and resource efficiency. This paper presents a comprehensive benchmarkingstudy comparing open-source LLMs with their non-open-source counterpartson the task of question answering. Our objective is to identify open-source al-ternatives capable of delivering comparable performance to proprietary mod-els while being lightweight in terms of resource requirements and suitable forCentral Processing Unit (CPU)-based inference. Through rigorous evalua-tion across various metrics including accuracy, inference speed, and resourceconsumption, we aim to provide insights into selecting efficient LLMs forreal-world applications. Our findings shed light on viable open-source al-ternatives that offer acceptable performance and efficiency, addressing thepressing need for accessible and efficient NLP solutions in industry settings.
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